ganDL

In this project, we'll use generative adversarial networks to generate new images of faces from celebrities!

Get the Data

We'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celeb dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

In [1]:
data_dir = './data'

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:00, 11.4MB/s]                            
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:22<00:00, 2.68KFile/s]
Downloading celeba: 1.44GB [02:12, 10.9MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits:

You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
/Users/v/.anaconda/lib/python3.6/site-packages/matplotlib/font_manager.py:280: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  'Matplotlib is building the font cache using fc-list. '
Out[2]:
<matplotlib.image.AxesImage at 0x112b53e10>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x11324cb38>

Preprocess the Data

Since the project's main focus is on building the GANs, we have to preprocess the data. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x8. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

We'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
/Users/v/.anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.
  

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder tensor of rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests


def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """

    # Image Placeholder
    real_inputs = tf.placeholder(tf.float32, (None, image_width, image_height,
                                              image_channels), 'real_inputs')
    
    # Z (?) Placeholder
    z_inputs = tf.placeholder(tf.float32, (None, z_dim), 'z_inputs')
    
    # Learning Rate
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return real_inputs, z_inputs, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images.

This function should be able to reuse the variables in the neural network. We use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused.

The function should return a tuple: (tensor output of the discriminator, tensor logits of the discriminator).

In [11]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        
        alpha = 0.1 # Alpa measure?
        
        
        # 28 x 28 x out_channel_dim
        
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        
        # 14 x 14 x 64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        # 7 x 7 x 128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        # 4 x 4 x 256
        
        flat = tf.reshape(relu3, (-1, 4 * 4 * 256)) # Flatten
        logits = tf.layers.dense(flat, 1) # Dense
        out = tf.sigmoid(logits) # Activstion

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

We implement generator to generate an image using z!

This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [12]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    with tf.variable_scope('generator', reuse=not is_train):
        alpha = 0.1
        
        x1 = tf.layers.dense(z, 7 * 7 * 512)
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        bn1 = tf.layers.batch_normalization(x1, training=is_train)
        relu1 = tf.maximum(alpha * bn1, bn1)
        
        # 7 x 7 x 512
        
        conv2 = tf.layers.conv2d_transpose(relu1, 256, 5, strides=1, padding='same')
        bn2 = tf.layers.batch_normalization(conv2, training=is_train)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        # 14 x 14 x 256
        
        conv3 = tf.layers.conv2d_transpose(relu2, 128, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(conv3, training=is_train)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        # 28 x 28 x 128
        
        logits = tf.layers.conv2d_transpose(relu3, out_channel_dim, 5, strides=2, padding='same')
        
        # 28 x 28 x out_channel_dim
        
        output = tf.tanh(logits)
    
    return output


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Here, we implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [17]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    
    g_model = generator(input_z, out_channel_dim, True)
    d_model_real, d_logits_real = discriminator(input_real, False)
    d_model_fake, d_logits_fake = discriminator(g_model, True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_real, labels=tf.ones_like(d_logits_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)))
    d_loss = d_loss_real + d_loss_fake
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))

    return d_loss, g_loss

    return None, None


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [18]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(
            learning_rate=learning_rate, beta1=beta1).minimize(
                d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(
            learning_rate=learning_rate, beta1=beta1).minimize(
                g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [19]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [20]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches,
          data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    ## Setup
    n_samples, image_width, image_height, image_channels = data_shape # Input Shape
    input_real, input_z, learn_rate = model_inputs(image_width, image_height,
                                                   image_channels, z_dim) # Input Real & Input Z
    
    d_loss, g_loss = model_loss(input_real, input_z, image_channels) # Discriminator and Generator Loss Functions
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1) # Model Options (Loss, Hyperparameters)
    iterations = int(n_samples / batch_size)
    
    show_every = 100 # Logging 
    print_every = 10 # Stuff
    n_images = 16    # Pretty
    
    ## Session
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer()) # run Session
        for epoch_i in range(epoch_count): 
           # From step 0 in epoch_i 
            step = 0
            # For image batch
            for batch_images in get_batches(batch_size):
                # Make two batchs
                batch_images *= 2
                # Increment Step
                step += 1
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                # Run Optimizers
                
                # Discriminator
                _ = sess.run(
                    d_opt, 
                    feed_dict={
                        input_real: batch_images,
                        input_z: batch_z,
                        learn_rate: learning_rate
                    })
                
                # Generator
                _ = sess.run(
                    g_opt,
                    feed_dict={
                        input_z: batch_z,
                        input_real: batch_images,
                        learn_rate: learning_rate
                    })
                # Print Loss
                if step % print_every == 0:
                    train_loss_d = d_loss.eval({
                        input_real: batch_images,
                        input_z: batch_z
                    }) # Discriminator looks at loss between input_real:training and input_Z:generated results
                    train_loss_g = g_loss.eval({input_z: batch_z}) # Generator looks at input_z and batch_z only
                    
                    print("Epoch {}/{}...".format(epoch_i + 1, epoch_count),
                          "Iter {}/{}...".format(step, iterations),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}...".format(train_loss_g))

                if step % show_every == 0:
                    # And Generated images!
                    show_generator_output(sess, n_images, input_z,
                                          image_channels, data_image_mode)

        print('finished.')

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [21]:
batch_size = 32
z_dim = 100
learning_rate = 0.001
beta1 = 0.5



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Iter 10/1875... Discriminator Loss: 4.0218... Generator Loss: 0.0646...
Epoch 1/2... Iter 20/1875... Discriminator Loss: 0.2783... Generator Loss: 3.2158...
Epoch 1/2... Iter 30/1875... Discriminator Loss: 0.7933... Generator Loss: 3.8098...
Epoch 1/2... Iter 40/1875... Discriminator Loss: 0.9701... Generator Loss: 4.1682...
Epoch 1/2... Iter 50/1875... Discriminator Loss: 0.0499... Generator Loss: 3.7431...
Epoch 1/2... Iter 60/1875... Discriminator Loss: 1.9527... Generator Loss: 0.6648...
Epoch 1/2... Iter 70/1875... Discriminator Loss: 1.5684... Generator Loss: 0.7748...
Epoch 1/2... Iter 80/1875... Discriminator Loss: 1.3684... Generator Loss: 0.7814...
Epoch 1/2... Iter 90/1875... Discriminator Loss: 1.2870... Generator Loss: 0.6251...
Epoch 1/2... Iter 100/1875... Discriminator Loss: 1.8320... Generator Loss: 0.2892...
Epoch 1/2... Iter 110/1875... Discriminator Loss: 1.4058... Generator Loss: 1.5618...
Epoch 1/2... Iter 120/1875... Discriminator Loss: 1.4036... Generator Loss: 0.7572...
Epoch 1/2... Iter 130/1875... Discriminator Loss: 1.7034... Generator Loss: 0.3316...
Epoch 1/2... Iter 140/1875... Discriminator Loss: 1.7068... Generator Loss: 0.3037...
Epoch 1/2... Iter 150/1875... Discriminator Loss: 1.9767... Generator Loss: 0.2150...
Epoch 1/2... Iter 160/1875... Discriminator Loss: 1.1966... Generator Loss: 0.5393...
Epoch 1/2... Iter 170/1875... Discriminator Loss: 1.4730... Generator Loss: 0.4161...
Epoch 1/2... Iter 180/1875... Discriminator Loss: 1.3813... Generator Loss: 0.7693...
Epoch 1/2... Iter 190/1875... Discriminator Loss: 1.3181... Generator Loss: 1.2597...
Epoch 1/2... Iter 200/1875... Discriminator Loss: 1.0536... Generator Loss: 1.0248...
Epoch 1/2... Iter 210/1875... Discriminator Loss: 1.3666... Generator Loss: 1.1520...
Epoch 1/2... Iter 220/1875... Discriminator Loss: 1.4027... Generator Loss: 0.4296...
Epoch 1/2... Iter 230/1875... Discriminator Loss: 1.0669... Generator Loss: 1.3381...
Epoch 1/2... Iter 240/1875... Discriminator Loss: 1.0560... Generator Loss: 0.7947...
Epoch 1/2... Iter 250/1875... Discriminator Loss: 1.4039... Generator Loss: 1.4736...
Epoch 1/2... Iter 260/1875... Discriminator Loss: 1.2684... Generator Loss: 0.5303...
Epoch 1/2... Iter 270/1875... Discriminator Loss: 1.6589... Generator Loss: 0.3135...
Epoch 1/2... Iter 280/1875... Discriminator Loss: 1.4404... Generator Loss: 0.4960...
Epoch 1/2... Iter 290/1875... Discriminator Loss: 1.1842... Generator Loss: 0.7624...
Epoch 1/2... Iter 300/1875... Discriminator Loss: 1.1856... Generator Loss: 0.6224...
Epoch 1/2... Iter 310/1875... Discriminator Loss: 1.1327... Generator Loss: 0.8131...
Epoch 1/2... Iter 320/1875... Discriminator Loss: 1.2899... Generator Loss: 1.0906...
Epoch 1/2... Iter 330/1875... Discriminator Loss: 1.1975... Generator Loss: 0.8950...
Epoch 1/2... Iter 340/1875... Discriminator Loss: 1.4925... Generator Loss: 0.6934...
Epoch 1/2... Iter 350/1875... Discriminator Loss: 1.4477... Generator Loss: 0.4922...
Epoch 1/2... Iter 360/1875... Discriminator Loss: 1.4005... Generator Loss: 0.5148...
Epoch 1/2... Iter 370/1875... Discriminator Loss: 1.1416... Generator Loss: 1.2231...
Epoch 1/2... Iter 380/1875... Discriminator Loss: 1.1410... Generator Loss: 0.8564...
Epoch 1/2... Iter 390/1875... Discriminator Loss: 1.1024... Generator Loss: 1.5837...
Epoch 1/2... Iter 400/1875... Discriminator Loss: 1.4436... Generator Loss: 0.9209...
Epoch 1/2... Iter 410/1875... Discriminator Loss: 1.3264... Generator Loss: 0.5588...
Epoch 1/2... Iter 420/1875... Discriminator Loss: 1.2088... Generator Loss: 0.5123...
Epoch 1/2... Iter 430/1875... Discriminator Loss: 1.2546... Generator Loss: 1.0865...
Epoch 1/2... Iter 440/1875... Discriminator Loss: 1.5032... Generator Loss: 0.3476...
Epoch 1/2... Iter 450/1875... Discriminator Loss: 1.7962... Generator Loss: 0.2518...
Epoch 1/2... Iter 460/1875... Discriminator Loss: 1.1728... Generator Loss: 0.6171...
Epoch 1/2... Iter 470/1875... Discriminator Loss: 1.0365... Generator Loss: 1.0054...
Epoch 1/2... Iter 480/1875... Discriminator Loss: 1.2845... Generator Loss: 0.4644...
Epoch 1/2... Iter 490/1875... Discriminator Loss: 1.3695... Generator Loss: 0.5008...
Epoch 1/2... Iter 500/1875... Discriminator Loss: 1.3965... Generator Loss: 0.4594...
Epoch 1/2... Iter 510/1875... Discriminator Loss: 1.0185... Generator Loss: 1.5612...
Epoch 1/2... Iter 520/1875... Discriminator Loss: 1.1077... Generator Loss: 0.7237...
Epoch 1/2... Iter 530/1875... Discriminator Loss: 1.3261... Generator Loss: 0.6331...
Epoch 1/2... Iter 540/1875... Discriminator Loss: 1.3081... Generator Loss: 0.4394...
Epoch 1/2... Iter 550/1875... Discriminator Loss: 1.1048... Generator Loss: 0.6653...
Epoch 1/2... Iter 560/1875... Discriminator Loss: 1.1241... Generator Loss: 1.1662...
Epoch 1/2... Iter 570/1875... Discriminator Loss: 1.7596... Generator Loss: 0.2954...
Epoch 1/2... Iter 580/1875... Discriminator Loss: 1.2759... Generator Loss: 0.6970...
Epoch 1/2... Iter 590/1875... Discriminator Loss: 1.7951... Generator Loss: 0.2467...
Epoch 1/2... Iter 600/1875... Discriminator Loss: 1.2167... Generator Loss: 0.5552...
Epoch 1/2... Iter 610/1875... Discriminator Loss: 1.3833... Generator Loss: 1.3289...
Epoch 1/2... Iter 620/1875... Discriminator Loss: 1.1242... Generator Loss: 0.8445...
Epoch 1/2... Iter 630/1875... Discriminator Loss: 1.3641... Generator Loss: 0.5894...
Epoch 1/2... Iter 640/1875... Discriminator Loss: 1.3949... Generator Loss: 0.3865...
Epoch 1/2... Iter 650/1875... Discriminator Loss: 0.9713... Generator Loss: 1.0150...
Epoch 1/2... Iter 660/1875... Discriminator Loss: 1.1846... Generator Loss: 0.6536...
Epoch 1/2... Iter 670/1875... Discriminator Loss: 1.1113... Generator Loss: 0.8097...
Epoch 1/2... Iter 680/1875... Discriminator Loss: 0.9116... Generator Loss: 0.9756...
Epoch 1/2... Iter 690/1875... Discriminator Loss: 1.0785... Generator Loss: 0.9423...
Epoch 1/2... Iter 700/1875... Discriminator Loss: 1.0012... Generator Loss: 0.7056...
Epoch 1/2... Iter 710/1875... Discriminator Loss: 1.0342... Generator Loss: 1.3071...
Epoch 1/2... Iter 720/1875... Discriminator Loss: 1.1919... Generator Loss: 0.5567...
Epoch 1/2... Iter 730/1875... Discriminator Loss: 1.3590... Generator Loss: 0.3926...
Epoch 1/2... Iter 740/1875... Discriminator Loss: 1.3128... Generator Loss: 0.4293...
Epoch 1/2... Iter 750/1875... Discriminator Loss: 1.2691... Generator Loss: 0.5561...
Epoch 1/2... Iter 760/1875... Discriminator Loss: 1.1476... Generator Loss: 0.6566...
Epoch 1/2... Iter 770/1875... Discriminator Loss: 1.2070... Generator Loss: 0.6793...
Epoch 1/2... Iter 780/1875... Discriminator Loss: 1.2103... Generator Loss: 0.8741...
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Epoch 2/2... Iter 1360/1875... Discriminator Loss: 1.0501... Generator Loss: 0.6915...
Epoch 2/2... Iter 1370/1875... Discriminator Loss: 2.2476... Generator Loss: 0.3459...
Epoch 2/2... Iter 1380/1875... Discriminator Loss: 1.9374... Generator Loss: 0.4401...
Epoch 2/2... Iter 1390/1875... Discriminator Loss: 0.9527... Generator Loss: 0.8789...
Epoch 2/2... Iter 1400/1875... Discriminator Loss: 1.0343... Generator Loss: 0.7332...
Epoch 2/2... Iter 1410/1875... Discriminator Loss: 0.3363... Generator Loss: 2.6305...
Epoch 2/2... Iter 1420/1875... Discriminator Loss: 0.3179... Generator Loss: 1.8078...
Epoch 2/2... Iter 1430/1875... Discriminator Loss: 1.8181... Generator Loss: 0.3269...
Epoch 2/2... Iter 1440/1875... Discriminator Loss: 0.2843... Generator Loss: 2.5229...
Epoch 2/2... Iter 1450/1875... Discriminator Loss: 0.7590... Generator Loss: 1.0143...
Epoch 2/2... Iter 1460/1875... Discriminator Loss: 0.8320... Generator Loss: 0.9533...
Epoch 2/2... Iter 1470/1875... Discriminator Loss: 1.9506... Generator Loss: 0.4825...
Epoch 2/2... Iter 1480/1875... Discriminator Loss: 1.5548... Generator Loss: 0.4426...
Epoch 2/2... Iter 1490/1875... Discriminator Loss: 0.5862... Generator Loss: 1.4702...
Epoch 2/2... Iter 1500/1875... Discriminator Loss: 0.8737... Generator Loss: 2.7342...
Epoch 2/2... Iter 1510/1875... Discriminator Loss: 0.7425... Generator Loss: 1.0397...
Epoch 2/2... Iter 1520/1875... Discriminator Loss: 0.4217... Generator Loss: 2.7103...
Epoch 2/2... Iter 1530/1875... Discriminator Loss: 0.7699... Generator Loss: 0.9537...
Epoch 2/2... Iter 1540/1875... Discriminator Loss: 1.7573... Generator Loss: 0.3704...
Epoch 2/2... Iter 1550/1875... Discriminator Loss: 0.7210... Generator Loss: 1.4785...
Epoch 2/2... Iter 1560/1875... Discriminator Loss: 0.5587... Generator Loss: 1.3368...
Epoch 2/2... Iter 1570/1875... Discriminator Loss: 1.6597... Generator Loss: 0.4010...
Epoch 2/2... Iter 1580/1875... Discriminator Loss: 0.3392... Generator Loss: 1.6815...
Epoch 2/2... Iter 1590/1875... Discriminator Loss: 0.7786... Generator Loss: 0.8373...
Epoch 2/2... Iter 1600/1875... Discriminator Loss: 0.5866... Generator Loss: 1.3244...
Epoch 2/2... Iter 1610/1875... Discriminator Loss: 0.4640... Generator Loss: 1.3483...
Epoch 2/2... Iter 1620/1875... Discriminator Loss: 1.5376... Generator Loss: 0.4164...
Epoch 2/2... Iter 1630/1875... Discriminator Loss: 0.2469... Generator Loss: 2.2332...
Epoch 2/2... Iter 1640/1875... Discriminator Loss: 3.5937... Generator Loss: 0.0738...
Epoch 2/2... Iter 1650/1875... Discriminator Loss: 0.8863... Generator Loss: 0.9043...
Epoch 2/2... Iter 1660/1875... Discriminator Loss: 0.6664... Generator Loss: 1.2278...
Epoch 2/2... Iter 1670/1875... Discriminator Loss: 0.4922... Generator Loss: 1.7797...
Epoch 2/2... Iter 1680/1875... Discriminator Loss: 0.2229... Generator Loss: 2.3550...
Epoch 2/2... Iter 1690/1875... Discriminator Loss: 0.5342... Generator Loss: 1.3230...
Epoch 2/2... Iter 1700/1875... Discriminator Loss: 0.9649... Generator Loss: 3.9383...
Epoch 2/2... Iter 1710/1875... Discriminator Loss: 0.4160... Generator Loss: 2.1134...
Epoch 2/2... Iter 1720/1875... Discriminator Loss: 1.8894... Generator Loss: 0.4648...
Epoch 2/2... Iter 1730/1875... Discriminator Loss: 0.6476... Generator Loss: 1.1439...
Epoch 2/2... Iter 1740/1875... Discriminator Loss: 1.4598... Generator Loss: 0.5721...
Epoch 2/2... Iter 1750/1875... Discriminator Loss: 0.4261... Generator Loss: 1.6577...
Epoch 2/2... Iter 1760/1875... Discriminator Loss: 0.4417... Generator Loss: 1.4721...
Epoch 2/2... Iter 1770/1875... Discriminator Loss: 0.3284... Generator Loss: 1.9066...
Epoch 2/2... Iter 1780/1875... Discriminator Loss: 3.2777... Generator Loss: 0.1291...
Epoch 2/2... Iter 1790/1875... Discriminator Loss: 0.6487... Generator Loss: 1.1718...
Epoch 2/2... Iter 1800/1875... Discriminator Loss: 1.0622... Generator Loss: 0.6470...
Epoch 2/2... Iter 1810/1875... Discriminator Loss: 0.6885... Generator Loss: 1.0449...
Epoch 2/2... Iter 1820/1875... Discriminator Loss: 0.4243... Generator Loss: 1.7199...
Epoch 2/2... Iter 1830/1875... Discriminator Loss: 0.1904... Generator Loss: 2.4484...
Epoch 2/2... Iter 1840/1875... Discriminator Loss: 2.4829... Generator Loss: 0.1865...
Epoch 2/2... Iter 1850/1875... Discriminator Loss: 0.7453... Generator Loss: 1.5051...
Epoch 2/2... Iter 1860/1875... Discriminator Loss: 0.3255... Generator Loss: 2.0396...
Epoch 2/2... Iter 1870/1875... Discriminator Loss: 1.9062... Generator Loss: 0.6009...
finished

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.001
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Iter 10/6331... Discriminator Loss: 4.9435... Generator Loss: 0.0111...
Epoch 1/1... Iter 20/6331... Discriminator Loss: 0.1462... Generator Loss: 11.0203...
Epoch 1/1... Iter 30/6331... Discriminator Loss: 0.1705... Generator Loss: 10.0624...
Epoch 1/1... Iter 40/6331... Discriminator Loss: 2.0683... Generator Loss: 0.3989...
Epoch 1/1... Iter 50/6331... Discriminator Loss: 1.0569... Generator Loss: 1.2973...
Epoch 1/1... Iter 60/6331... Discriminator Loss: 2.3848... Generator Loss: 0.1865...
Epoch 1/1... Iter 70/6331... Discriminator Loss: 0.7900... Generator Loss: 0.8425...
Epoch 1/1... Iter 80/6331... Discriminator Loss: 1.9186... Generator Loss: 0.2633...
Epoch 1/1... Iter 90/6331... Discriminator Loss: 1.1366... Generator Loss: 0.6761...
Epoch 1/1... Iter 100/6331... Discriminator Loss: 0.8042... Generator Loss: 1.0598...
Epoch 1/1... Iter 110/6331... Discriminator Loss: 0.9160... Generator Loss: 1.1567...
Epoch 1/1... Iter 120/6331... Discriminator Loss: 0.8506... Generator Loss: 1.1220...
Epoch 1/1... Iter 130/6331... Discriminator Loss: 1.1633... Generator Loss: 0.6851...
Epoch 1/1... Iter 140/6331... Discriminator Loss: 0.5615... Generator Loss: 1.2357...
Epoch 1/1... Iter 150/6331... Discriminator Loss: 0.8108... Generator Loss: 0.7773...
Epoch 1/1... Iter 160/6331... Discriminator Loss: 0.7562... Generator Loss: 0.9241...
Epoch 1/1... Iter 170/6331... Discriminator Loss: 1.1998... Generator Loss: 0.9015...
Epoch 1/1... Iter 180/6331... Discriminator Loss: 1.7665... Generator Loss: 0.6368...
Epoch 1/1... Iter 190/6331... Discriminator Loss: 1.2720... Generator Loss: 0.5199...
Epoch 1/1... Iter 200/6331... Discriminator Loss: 1.5448... Generator Loss: 0.4933...
Epoch 1/1... Iter 210/6331... Discriminator Loss: 0.9813... Generator Loss: 0.6569...
Epoch 1/1... Iter 220/6331... Discriminator Loss: 1.1050... Generator Loss: 0.7705...
Epoch 1/1... Iter 230/6331... Discriminator Loss: 1.6899... Generator Loss: 0.3347...
Epoch 1/1... Iter 240/6331... Discriminator Loss: 1.2372... Generator Loss: 0.6847...
Epoch 1/1... Iter 250/6331... Discriminator Loss: 2.0385... Generator Loss: 1.8960...
Epoch 1/1... Iter 260/6331... Discriminator Loss: 0.5046... Generator Loss: 1.1757...
Epoch 1/1... Iter 270/6331... Discriminator Loss: 0.7617... Generator Loss: 1.2786...
Epoch 1/1... Iter 280/6331... Discriminator Loss: 2.7737... Generator Loss: 3.4503...
Epoch 1/1... Iter 290/6331... Discriminator Loss: 1.5559... Generator Loss: 0.4201...
Epoch 1/1... Iter 300/6331... Discriminator Loss: 1.4237... Generator Loss: 1.5149...
Epoch 1/1... Iter 310/6331... Discriminator Loss: 1.3033... Generator Loss: 1.1321...
Epoch 1/1... Iter 320/6331... Discriminator Loss: 1.9583... Generator Loss: 0.2111...
Epoch 1/1... Iter 330/6331... Discriminator Loss: 1.5232... Generator Loss: 1.4733...
Epoch 1/1... Iter 340/6331... Discriminator Loss: 1.4514... Generator Loss: 0.6956...
Epoch 1/1... Iter 350/6331... Discriminator Loss: 1.2008... Generator Loss: 0.5576...
Epoch 1/1... Iter 360/6331... Discriminator Loss: 1.4084... Generator Loss: 0.5772...
Epoch 1/1... Iter 370/6331... Discriminator Loss: 1.5748... Generator Loss: 0.8269...
Epoch 1/1... Iter 380/6331... Discriminator Loss: 2.1252... Generator Loss: 0.2514...
Epoch 1/1... Iter 390/6331... Discriminator Loss: 1.5518... Generator Loss: 0.5630...
Epoch 1/1... Iter 400/6331... Discriminator Loss: 0.5867... Generator Loss: 1.8508...
Epoch 1/1... Iter 410/6331... Discriminator Loss: 1.3831... Generator Loss: 0.6691...
Epoch 1/1... Iter 420/6331... Discriminator Loss: 1.2492... Generator Loss: 1.1267...
Epoch 1/1... Iter 430/6331... Discriminator Loss: 1.3442... Generator Loss: 0.4854...
Epoch 1/1... Iter 440/6331... Discriminator Loss: 1.2379... Generator Loss: 1.1500...
Epoch 1/1... Iter 450/6331... Discriminator Loss: 1.2349... Generator Loss: 0.7036...
Epoch 1/1... Iter 460/6331... Discriminator Loss: 0.7437... Generator Loss: 0.8697...
Epoch 1/1... Iter 470/6331... Discriminator Loss: 1.3711... Generator Loss: 0.5786...
Epoch 1/1... Iter 480/6331... Discriminator Loss: 2.0428... Generator Loss: 1.3934...
Epoch 1/1... Iter 490/6331... Discriminator Loss: 0.7746... Generator Loss: 0.8791...
Epoch 1/1... Iter 500/6331... Discriminator Loss: 1.4276... Generator Loss: 0.7782...
Epoch 1/1... Iter 510/6331... Discriminator Loss: 1.2628... Generator Loss: 0.4756...
Epoch 1/1... Iter 520/6331... Discriminator Loss: 1.2047... Generator Loss: 0.6173...
Epoch 1/1... Iter 530/6331... Discriminator Loss: 1.4788... Generator Loss: 2.2403...
Epoch 1/1... Iter 540/6331... Discriminator Loss: 0.9871... Generator Loss: 0.5785...
Epoch 1/1... Iter 550/6331... Discriminator Loss: 1.0776... Generator Loss: 1.4421...
Epoch 1/1... Iter 560/6331... Discriminator Loss: 1.7609... Generator Loss: 0.5050...
Epoch 1/1... Iter 570/6331... Discriminator Loss: 2.2232... Generator Loss: 0.1691...
Epoch 1/1... Iter 580/6331... Discriminator Loss: 1.3740... Generator Loss: 0.5801...
Epoch 1/1... Iter 590/6331... Discriminator Loss: 0.8259... Generator Loss: 1.9174...
Epoch 1/1... Iter 600/6331... Discriminator Loss: 1.2513... Generator Loss: 0.8055...
Epoch 1/1... Iter 610/6331... Discriminator Loss: 1.4740... Generator Loss: 0.4777...
Epoch 1/1... Iter 620/6331... Discriminator Loss: 1.7792... Generator Loss: 0.4420...
Epoch 1/1... Iter 630/6331... Discriminator Loss: 0.9787... Generator Loss: 0.7992...
Epoch 1/1... Iter 640/6331... Discriminator Loss: 1.4579... Generator Loss: 0.8433...
Epoch 1/1... Iter 650/6331... Discriminator Loss: 1.3479... Generator Loss: 0.5197...
Epoch 1/1... Iter 660/6331... Discriminator Loss: 1.8586... Generator Loss: 1.5689...
Epoch 1/1... Iter 670/6331... Discriminator Loss: 1.8666... Generator Loss: 0.3085...
Epoch 1/1... Iter 680/6331... Discriminator Loss: 1.3581... Generator Loss: 1.3076...
Epoch 1/1... Iter 690/6331... Discriminator Loss: 1.3635... Generator Loss: 0.8948...
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Epoch 1/1... Iter 710/6331... Discriminator Loss: 1.3298... Generator Loss: 0.5334...
Epoch 1/1... Iter 720/6331... Discriminator Loss: 1.3702... Generator Loss: 0.8899...
Epoch 1/1... Iter 730/6331... Discriminator Loss: 1.2461... Generator Loss: 0.6825...
Epoch 1/1... Iter 740/6331... Discriminator Loss: 1.3730... Generator Loss: 0.7003...
Epoch 1/1... Iter 750/6331... Discriminator Loss: 1.6868... Generator Loss: 1.8004...
Epoch 1/1... Iter 760/6331... Discriminator Loss: 1.0484... Generator Loss: 0.8179...
Epoch 1/1... Iter 770/6331... Discriminator Loss: 1.2864... Generator Loss: 0.8533...
Epoch 1/1... Iter 780/6331... Discriminator Loss: 1.2003... Generator Loss: 0.5573...
Epoch 1/1... Iter 790/6331... Discriminator Loss: 1.9824... Generator Loss: 0.2554...
Epoch 1/1... Iter 800/6331... Discriminator Loss: 0.9247... Generator Loss: 0.7153...
Epoch 1/1... Iter 810/6331... Discriminator Loss: 1.3748... Generator Loss: 0.5727...
Epoch 1/1... Iter 820/6331... Discriminator Loss: 1.7021... Generator Loss: 0.3463...
Epoch 1/1... Iter 830/6331... Discriminator Loss: 0.8317... Generator Loss: 0.8885...
Epoch 1/1... Iter 840/6331... Discriminator Loss: 1.6064... Generator Loss: 0.3340...
Epoch 1/1... Iter 850/6331... Discriminator Loss: 1.4434... Generator Loss: 0.8086...
Epoch 1/1... Iter 860/6331... Discriminator Loss: 1.5192... Generator Loss: 0.6522...
Epoch 1/1... Iter 870/6331... Discriminator Loss: 1.6759... Generator Loss: 0.8560...
Epoch 1/1... Iter 880/6331... Discriminator Loss: 1.1810... Generator Loss: 0.9141...
Epoch 1/1... Iter 890/6331... Discriminator Loss: 1.4585... Generator Loss: 1.0171...
Epoch 1/1... Iter 900/6331... Discriminator Loss: 1.3180... Generator Loss: 0.6542...
Epoch 1/1... Iter 910/6331... Discriminator Loss: 1.4436... Generator Loss: 0.7268...
Epoch 1/1... Iter 920/6331... Discriminator Loss: 1.3083... Generator Loss: 0.5424...
Epoch 1/1... Iter 930/6331... Discriminator Loss: 1.4735... Generator Loss: 0.7318...
Epoch 1/1... Iter 940/6331... Discriminator Loss: 1.1688... Generator Loss: 0.7537...
Epoch 1/1... Iter 950/6331... Discriminator Loss: 1.7994... Generator Loss: 1.0175...
Epoch 1/1... Iter 960/6331... Discriminator Loss: 1.2386... Generator Loss: 0.8194...
Epoch 1/1... Iter 970/6331... Discriminator Loss: 1.0587... Generator Loss: 0.6111...
Epoch 1/1... Iter 980/6331... Discriminator Loss: 1.2526... Generator Loss: 0.8668...
Epoch 1/1... Iter 990/6331... Discriminator Loss: 1.3513... Generator Loss: 0.5152...
Epoch 1/1... Iter 1000/6331... Discriminator Loss: 1.2218... Generator Loss: 1.2082...
Epoch 1/1... Iter 1010/6331... Discriminator Loss: 1.5995... Generator Loss: 0.9916...
Epoch 1/1... Iter 1020/6331... Discriminator Loss: 0.8986... Generator Loss: 0.8461...
Epoch 1/1... Iter 1030/6331... Discriminator Loss: 1.4173... Generator Loss: 0.8135...
Epoch 1/1... Iter 1040/6331... Discriminator Loss: 1.2070... Generator Loss: 0.6992...
Epoch 1/1... Iter 1050/6331... Discriminator Loss: 1.1757... Generator Loss: 0.8210...
Epoch 1/1... Iter 1060/6331... Discriminator Loss: 1.7687... Generator Loss: 0.5088...
Epoch 1/1... Iter 1070/6331... Discriminator Loss: 1.4024... Generator Loss: 0.6592...
Epoch 1/1... Iter 1080/6331... Discriminator Loss: 1.2795... Generator Loss: 0.6291...
Epoch 1/1... Iter 1090/6331... Discriminator Loss: 1.4565... Generator Loss: 0.7902...
Epoch 1/1... Iter 1100/6331... Discriminator Loss: 1.7081... Generator Loss: 0.3271...
Epoch 1/1... Iter 1110/6331... Discriminator Loss: 1.4257... Generator Loss: 0.8070...
Epoch 1/1... Iter 1120/6331... Discriminator Loss: 1.2506... Generator Loss: 0.7314...
Epoch 1/1... Iter 1130/6331... Discriminator Loss: 1.2549... Generator Loss: 0.7618...
Epoch 1/1... Iter 1140/6331... Discriminator Loss: 1.1124... Generator Loss: 0.8039...
Epoch 1/1... Iter 1150/6331... Discriminator Loss: 1.3939... Generator Loss: 0.4280...
Epoch 1/1... Iter 1160/6331... Discriminator Loss: 1.2175... Generator Loss: 0.8316...
Epoch 1/1... Iter 1170/6331... Discriminator Loss: 1.2221... Generator Loss: 0.9372...
Epoch 1/1... Iter 1180/6331... Discriminator Loss: 1.6073... Generator Loss: 0.7260...
Epoch 1/1... Iter 1190/6331... Discriminator Loss: 1.7710... Generator Loss: 0.9013...
Epoch 1/1... Iter 1200/6331... Discriminator Loss: 1.2403... Generator Loss: 0.7120...
Epoch 1/1... Iter 1210/6331... Discriminator Loss: 1.7350... Generator Loss: 0.3196...
Epoch 1/1... Iter 1220/6331... Discriminator Loss: 1.5771... Generator Loss: 0.8784...
Epoch 1/1... Iter 1230/6331... Discriminator Loss: 1.1732... Generator Loss: 0.9593...
Epoch 1/1... Iter 1240/6331... Discriminator Loss: 1.2664... Generator Loss: 0.7098...
Epoch 1/1... Iter 1250/6331... Discriminator Loss: 1.1852... Generator Loss: 0.7169...
Epoch 1/1... Iter 1260/6331... Discriminator Loss: 1.4821... Generator Loss: 0.3478...
Epoch 1/1... Iter 1270/6331... Discriminator Loss: 1.6708... Generator Loss: 0.3162...
Epoch 1/1... Iter 1280/6331... Discriminator Loss: 1.3719... Generator Loss: 0.7572...
Epoch 1/1... Iter 1290/6331... Discriminator Loss: 1.4613... Generator Loss: 0.8302...
Epoch 1/1... Iter 1300/6331... Discriminator Loss: 1.1004... Generator Loss: 0.7009...
Epoch 1/1... Iter 1310/6331... Discriminator Loss: 1.1128... Generator Loss: 0.9943...
Epoch 1/1... Iter 1320/6331... Discriminator Loss: 1.1864... Generator Loss: 0.8367...
Epoch 1/1... Iter 1330/6331... Discriminator Loss: 1.1100... Generator Loss: 1.0390...
Epoch 1/1... Iter 1340/6331... Discriminator Loss: 1.0988... Generator Loss: 0.7168...
Epoch 1/1... Iter 1350/6331... Discriminator Loss: 1.2151... Generator Loss: 0.6987...
Epoch 1/1... Iter 1360/6331... Discriminator Loss: 0.9920... Generator Loss: 0.8759...
Epoch 1/1... Iter 1370/6331... Discriminator Loss: 1.5106... Generator Loss: 1.1100...
Epoch 1/1... Iter 1380/6331... Discriminator Loss: 1.3449... Generator Loss: 0.4319...
Epoch 1/1... Iter 1390/6331... Discriminator Loss: 1.3827... Generator Loss: 0.9415...
Epoch 1/1... Iter 1400/6331... Discriminator Loss: 1.3098... Generator Loss: 0.8565...
Epoch 1/1... Iter 1410/6331... Discriminator Loss: 1.4123... Generator Loss: 0.6278...
Epoch 1/1... Iter 1420/6331... Discriminator Loss: 1.0819... Generator Loss: 0.6580...
Epoch 1/1... Iter 1430/6331... Discriminator Loss: 1.1155... Generator Loss: 0.5950...
Epoch 1/1... Iter 1440/6331... Discriminator Loss: 1.2844... Generator Loss: 0.9851...
Epoch 1/1... Iter 1450/6331... Discriminator Loss: 1.3997... Generator Loss: 0.5526...
Epoch 1/1... Iter 1460/6331... Discriminator Loss: 1.0253... Generator Loss: 0.6839...
Epoch 1/1... Iter 1470/6331... Discriminator Loss: 1.2165... Generator Loss: 0.6743...
Epoch 1/1... Iter 1480/6331... Discriminator Loss: 1.8742... Generator Loss: 0.3130...
Epoch 1/1... Iter 1490/6331... Discriminator Loss: 0.9899... Generator Loss: 0.8191...
Epoch 1/1... Iter 1500/6331... Discriminator Loss: 1.4351... Generator Loss: 0.6594...
Epoch 1/1... Iter 1510/6331... Discriminator Loss: 1.2586... Generator Loss: 0.8528...
Epoch 1/1... Iter 1520/6331... Discriminator Loss: 1.5169... Generator Loss: 0.5008...
Epoch 1/1... Iter 1530/6331... Discriminator Loss: 1.1804... Generator Loss: 0.8717...
Epoch 1/1... Iter 1540/6331... Discriminator Loss: 1.0524... Generator Loss: 0.7966...
Epoch 1/1... Iter 1550/6331... Discriminator Loss: 1.2962... Generator Loss: 0.8326...
Epoch 1/1... Iter 1560/6331... Discriminator Loss: 1.3541... Generator Loss: 0.6729...
Epoch 1/1... Iter 1570/6331... Discriminator Loss: 1.0473... Generator Loss: 0.8842...
Epoch 1/1... Iter 1580/6331... Discriminator Loss: 1.5520... Generator Loss: 0.4577...
Epoch 1/1... Iter 1590/6331... Discriminator Loss: 1.1125... Generator Loss: 0.6955...
Epoch 1/1... Iter 1600/6331... Discriminator Loss: 1.4823... Generator Loss: 0.3930...
Epoch 1/1... Iter 1610/6331... Discriminator Loss: 1.4393... Generator Loss: 0.5084...
Epoch 1/1... Iter 1620/6331... Discriminator Loss: 1.1792... Generator Loss: 0.5840...
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Epoch 1/1... Iter 1640/6331... Discriminator Loss: 1.3465... Generator Loss: 0.6843...
Epoch 1/1... Iter 1650/6331... Discriminator Loss: 1.2636... Generator Loss: 0.6981...
Epoch 1/1... Iter 1660/6331... Discriminator Loss: 1.2137... Generator Loss: 0.7285...
Epoch 1/1... Iter 1670/6331... Discriminator Loss: 1.4923... Generator Loss: 0.6052...
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Epoch 1/1... Iter 1700/6331... Discriminator Loss: 1.3236... Generator Loss: 0.9611...
Epoch 1/1... Iter 1710/6331... Discriminator Loss: 1.2769... Generator Loss: 0.9457...
Epoch 1/1... Iter 1720/6331... Discriminator Loss: 1.4024... Generator Loss: 0.5562...
Epoch 1/1... Iter 1730/6331... Discriminator Loss: 1.0356... Generator Loss: 0.7981...
Epoch 1/1... Iter 1740/6331... Discriminator Loss: 1.6209... Generator Loss: 1.4763...
Epoch 1/1... Iter 1750/6331... Discriminator Loss: 1.1473... Generator Loss: 0.7399...
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Epoch 1/1... Iter 1780/6331... Discriminator Loss: 1.2749... Generator Loss: 0.5343...
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Epoch 1/1... Iter 1800/6331... Discriminator Loss: 1.3571... Generator Loss: 0.7750...
Epoch 1/1... Iter 1810/6331... Discriminator Loss: 1.1451... Generator Loss: 0.8415...
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